1. Field of the Invention
The present invention relates to image processing methods for processing multivalued density data of an optically read image for half-tone image reproduction, which can be applied to such image processing apparatuses as a facsimile machine and image scanner.
2. Description of the Related Art
Conventionally, image processing apparatuses such as a facsimile machine for reproducing an optically read image as a binary-coded image employ various gray scale image processing methods to reproduce half-tone images such as photograph. One of such half-tone image processing methods is an error diffusion technique.
In accordance with the error diffusion technique, when the density of an object pixel is converted into binary-coded data, a binary-coding error (which means a difference between the multivalued density data before the binary-coding of the pixel and the density data after the binary-coding) is calculated. In the binary-coding process, the density data of the object pixel is compared with a predetermined threshold value, and then it is determined whether the pixel is a black pixel or a white pixel. Therefore, the binary-coding of a pixel having an intermediate density data inevitably accompanies an error or a difference between the density data before and after the binary-coding.
The binary-coding error is properly weighted and distributed to peripheral pixels. In the binary-coding process of one object pixel, the density data of the object pixel and binary-coding errors distributed from the peripheral pixels are summed, and the sum is compared with a predetermined binary-coding threshold value.
For example, when a reading operation is carried out in a main scanning direction by a scanner of a facsimile machine or the like, and the density of a pixel PO located on a line extending in the main scanning direction as shown in FIG. 2 is converted into a binary-coded data, the aforementioned binary-coding error occurs. This binary-coding error is multiplied by an error diffusion coefficient of 1/4 or 1/8, and distributed to peripheral pixels P1 to P6 around the pixel P0.
With a focus on one object pixel to which binary-coding errors are distributed, on the other hand, binary-coding errors of peripheral pixels B, C, D, E, F and G are distributed to the object pixel A, as shown in FIG. 3. Therefore, the binary-coding process for the object pixel A is based on a sum obtained by adding an error sum of binary-coding errors distributed to the object pixel A from the peripheral pixels B, C, D, E, F and G to the density data of the object pixel A.
Thus, the half-tone image reproduction is achieved by distributing a binary-coding error of each pixel to the peripheral pixels.
The binary-coding process is performed on the basis of a predetermined threshold value. If 256-scale multivalued data (white: 0, black: 255) is to be converted into binary-coded data, for example, the binary-coding threshold value is set to the median value therebetween, i.e., 128. The judgement of whether the object pixel is a white pixel or a black pixel is based on the density value of the object pixel A and the error sum obtained by adding up the binary-coding errors distributed to the object pixel A from the peripheral pixels B, C, D, E, F and G, with reference to the following inequalities (1) and (2).
Black judgement: EQU (Error sum)+(Density value of object pixel).gtoreq.128 (1)
White judgement: EQU (Error sum)+(Density value of object pixel)&lt;128 (2)
After the binary-coding process, the binary-coding error of the object pixel is calculated from the following equation (3) or (4). ##EQU1##
As described above, the binary-coding process is carried out on the basis of a predetermined threshold value. The binary-coding process based on the predetermined threshold value is advantageous in that the errors are balancedly distributed over the pixels. However, this binary-coding process has a disadvantage that the density of a half-tone image cannot be intentionally modified. That is, the half-tone image cannot be intentionally modified into a more whitish or blackish image. Therefore, it is difficult to correct a variation in image processing characteristics among different apparatuses, and to reproduce an image with a proper density according to an original image or the preference of a user.